Intelligent transportation systems, such as collision-avoidance systems, provide various levels of warning to automobile drivers to increase their safety and/or to reduce vehicular accidents. Various techniques have been developed to gather information about a vehicle's path of motion, other vehicles' locations, and/or the surrounding environment. For example, vision-based pedestrian-detection systems determine pedestrian locations in order to inform drivers of the distance between the vehicle and the pedestrians or the speed reduction required to avoid hitting the pedestrians. In fact, due to widespread pedestrian injury in relatively dense population environments (e.g., urban area), systems capable of detecting and alerting the driver to the presence of pedestrians in the path of the vehicle have been incorporated in collision-avoidance systems. In a typical pedestrian-detection system, a camera on the front of a vehicle captures images of the surrounding environment and image-processing software identifies pedestrians in the images.
Various approaches have been used to detect pedestrians in an acquired image. For example, one approach identifies pedestrians by detecting, over a series of images, the periodic motion characteristic of a person walking or running; because multiple images must be analyzed, the process is slow. Another approach recognizes pedestrians utilizing integration of texture information (e.g., image structures), template matching, and “inverse perspective mapping” (IPM). These approaches may provide robustness in identifying and tracking pedestrians, but depend on a template for the human walking model for template matching based, for example, on the Hausdorff distance (which measures how far two subsets of a metric space are from each other). To detect pedestrians having different walking styles, more than one walking model may be necessary. “Shape-based” systems attempt to identify a human shape among various object shapes in an image. The shape-based approach typically includes a classifier that can be trained to recognize a human shape among various other shapes in a set of training images. This training process may be time consuming and/or difficult, particularly if the image quality is poor (e.g., due to low contrast). So-called “neural nets” have also been proposed to detect and locate pedestrians. Although any of these approaches may be used with some degree of success, they generally involve intensive computation and thus require excessive processing time and/or high-performance processing systems to operate effectively. This may result in significant delays in real-time pedestrian detection and lead to safety hazards.
Consequently, there is a need for a fast approach to pedestrian detection that can be employed onboard a vehicle using a low-cost processing system.